Xem 1-16 trên 16 kết quả Stationarity
  • The aim of this textbook is to provide a step-by-step guide to financial econometrics using EViews 6.0 statistical package. It contains brief overviews of econometric concepts, models and data analysis techniques followed by empirical examples of how they can be implemented in EViews. This book is written as a compendium for undergraduate and graduate students in economics and finance. It also can serve as a guide for researchers and practitioners who desire to use EViews for analysing financial data.

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  • Chapter 14 Unit Roots and Cointegration 14.1 Introduction In this chapter, we turn our attention to models for a particular type of nonstationary time series. For present purposes, the usual definition of covariance stationarity is too strict. We consider instead an asymptotic version

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  • Signals and Signal Spaces The goal of this chapter is to give a brief overview of methods for characterizing signals and for describing their properties. Wewill start with a discussion of signal spaces such as Hilbert spaces, normed and metric spaces. Then, the energy density and correlation function of deterministic signals will be discussed. The remainder of this chapter is dedicated to random signals, which are encountered in almost all areas of signal processing. Here, basic concepts such as stationarity, autocorrelation, and power spectral densitywill be discussed. ...

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  • The great advantage of using panel data over a simple cross-sectional sample is that one can control for the country-specific fixed effects ai. Failure to do so leads to biased estimates if these fixed or latent effects are correlated with the explanatory variables, as is likely to be the case. However, unfortunately the use of panel data also leads to more compli- cations if some or all of the variables in the estimating equation follow a trend over time. Such trending typically implies what econometricians call non-stationarity.

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  • Only statistical inference with stationary variables provides valid results. In simple words, this is because if variables are non-stationary then any correlation between the explanatory and the dependent variable could be due to the trending in both variables that is caused by a third variable not included in the model. We tested for the non-stationarity of the variables in our model formally with the help of Levin, Lin and Chu’s (2002) unit root test for panel data.

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  • Cointegration and error correction Professor Roy Batchelor City University Business School, London & ESCP, Paris EVIEWS r On the City University system, EVIEWS 3.1 is in Start/ Programs/ Departmental Software/CUBS r Analysing stationarity in a single variable using VIEW r Analysing cointegration among a group of variables r Estimating an ECM model r Estimating a VAR-ECM model

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  • In this chapter, students will be able to understand: Highlight the problems that may occur if non-stationary data are used in their levels form, test for unit roots, examine whether systems of variables are cointegrated, estimate error correction and vector error correction models, explain the intuition behind Johansen’s test for cointegration,...

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  • This paper addresses the problem of blind separation of non stationary signals. We introduce an online separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter.

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  • It is past time to abandon significance testing. In case there is any reluctance to embrace this decision, proofs against the validity of testing to make decisions or to identify cause are given. In their place, models should be cast in their reality-based, predictive form.

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  • The study examined taxation and income inequality (GINI), specifically, it determined the impact of Value Added Tax (VAT), Custom and Excise Duties (CED), Petroleum Profit Tax (PPT) and Company Income Tax (CIT) on GINI in Nigeria from the year 1990 to 2016. The Cointegration and Error Correction Models (ECMs) were used to analyze the data. Augmented Dickey Fuller unit root was used to test for stationarity.

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  • The study was conducted to forecast the price of black pepper in one of the major markets of Karnataka state as the state ranks first position in production of pepper in India. The Gonikoppal market in Kodagu district was selected purposively on the basis of highest area and production in the state. The monthly prices of black pepper in Gonikoppal market were collected from the Karnataka State Agricultural Marketing Board, Bangalore, Karnataka state for the year 2008-09 to 2017-18.

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  • The study uses autoregressive fractionally integrated moving average – fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) models and chaos effects to determine nonlinearity properties present on currency ETN returns. The results find that the volatilities of currency ETNs have long-memory, non-stationarity and non-invertibility properties. These findings make the research conclude that mean reversion is a possibility and that the efficient market hypothesis of Fama (1970) became ungrounded on these investment instruments.

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  • The climatic parameters such as Rainfall, mean daily temperature, Wind Speed, relative humidity are spatial and changing with time space and position. That directly affects the availability of basic resources such as surface runoff, vegetation, soil health and quality needed for betterment of sustainability of human being. The predicting the surface runoff is complex and uncertain. It depended of these climatic parameters. Hence, their behaviour needed to be understood before putting in the hydrologic model.

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  • This study documents the impact of price variations in global markets, specifically oil, on stock returns at Pakistan stock exchange (PSX). We select three global markets (oil, gold and currency exchange) and two PSX indices (conventional and Islamic) for a period 2009-2020 to provide evidence. Monthly data for the selected time series is used for analysis. Analysis techniques include descriptive statistics, stationarity testing, Johansen cointegration, correlation and regression analysis.

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  • The study investigated stock market reactions to oil price fluctuations in Nigeria. A longitudinal design consisting of data on the Nigerian Stock market index, crude oil prices, exchange rate, interest rate, inflation rate and GDP for the period 1984-2019 was employed. The data were subjected to stationarity and cointegration tests using ADF and Johansen’s techniques. Based on the results of the stationarity and cointegration tests, Vector error correction model was used to analyse the research data.

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  • An approach to the formation of the voice signal (VS) informative features of the Vietnamese language on the basis of stationary autoregressive model coefficients is described. An original algorithm of VS segmentation based on interval estimation of speech sample numerical characteristics was developed to form local stationarity areas of the voice signal.

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